{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pima Indians Diabetes Data Set数据探索\n",
    "\n",
    "数据说明：\n",
    "Pima Indians Diabetes Data Set（皮马印第安人糖尿病数据集） 根据现有的医疗信息预测5年内皮马印第安人糖尿病发作的概率。   \n",
    "\n",
    "数据集共9个字段: \n",
    "0列为pregnants(怀孕次数)；\n",
    "1列为Plasma_glucose_concentration(口服葡萄糖耐量试验中2小时后的血浆葡萄糖浓度)；\n",
    "2列为blood_pressure(舒张压,单位:mm Hg）\n",
    "3列为Triceps_skin_fold_thickness(三头肌皮褶厚度,单位：mm）\n",
    "4列为serum_insulin(餐后血清胰岛素,单位:mm）\n",
    "5列为BMI,体重指数（体重（公斤）/ 身高（米）^2）\n",
    "6列为Diabetes_pedigree_function(糖尿病家系作用)\n",
    "7列为Age(年龄)\n",
    "8列为Target(分类变量,0或1）\n",
    " \n",
    "数据链接：https://archive.ics.uci.edu/ml/datasets/Pima+Indians+Diabetes\n",
    "\n",
    "p.s.: Kaggle也有一个Practice Fusion Diabetes Classification任务，可以试试:)\n",
    "https://www.kaggle.com/c/pf2012-diabetes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#导入必要工具包\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv(\"diabetes.csv\")\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(768, 9)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看数据规模\n",
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "Pregnancies                 768 non-null int64\n",
      "Glucose                     768 non-null int64\n",
      "BloodPressure               768 non-null int64\n",
      "SkinThickness               768 non-null int64\n",
      "Insulin                     768 non-null int64\n",
      "BMI                         768 non-null float64\n",
      "DiabetesPedigreeFunction    768 non-null float64\n",
      "Age                         768 non-null int64\n",
      "Outcome                     768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "#查看数据基本信息\n",
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
       "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    31.992578                  0.471876   33.240885    0.348958  \n",
       "std      7.884160                  0.331329   11.760232    0.476951  \n",
       "min      0.000000                  0.078000   21.000000    0.000000  \n",
       "25%     27.300000                  0.243750   24.000000    0.000000  \n",
       "50%     32.000000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看数值型特征的基本统计量\n",
    "train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "很多列最小值为0，有些列最小值是没有意义的，表名该列值是缺失值\n",
    "1、血浆葡萄糖浓度\n",
    "2、舒张压\n",
    "3、肱三头肌皮皱厚度\n",
    "4、餐后血清胰岛素\n",
    "5、体重指数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Glucose            5\n",
      "BloodPressure     35\n",
      "SkinThickness    227\n",
      "Insulin          374\n",
      "BMI               11\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "NaN_col_names = ['Glucose','BloodPressure','SkinThickness','Insulin','BMI']\n",
    "print((train[NaN_col_names] == 0).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Number of occurrences')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#该问题为分类问题，类别型特征直方图可以用countplot\n",
    "sns.countplot(train.Outcome)\n",
    "plt.xlabel('Diabetes')\n",
    "plt.ylabel('Number of occurrences')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pregnancies\n",
    "怀孕次数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Number of occurrences')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.countplot(train.Pregnancies)\n",
    "plt.xlabel('Number of Pregnancies')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xbd0cc18>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#怀孕次数和是否糖尿病的关系\n",
    "sns.countplot(x=\"Pregnancies\" , hue=\"Outcome\",data = train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Glucose\n",
    "血浆葡萄糖浓度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\AI\\Environment\\Anaconda2\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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jYl172DrguEnHJknaaqp9EElWAY8HNgAHVNVmaJII8LAFXnNqko1JNs7NzU0qVEladqaWIJI8EPgk8Oqq6j11R1WdVVVrqmrNypUrxxegJC1zUxkHkWQPmuTw0ar6VFt8e5IDq2pzkgOBLdOITZKGtdTGTkzjLqYAHwSuq6p3DvzoQmBtu70WuGDSsUmStppGC+IpwIuBbya5si37feBPgY8nOQW4BThhCrFpxgzzF5mknWPiCaKq/h7IAj8+apKxSJIW5lxMkjRFw7aSJ9F34VQbkqROJghJUicThCSpkwlCktTJBCFJ6mSCkCR1MkFIkjqZICRJnUwQkqROJghJUicThCSpkwlCktTJBCFJ6mSCkCR1MkFIkjqZICRJnUwQkqROJghJUicThCSpkwlCktRpxbQD0PIx7OLskqbDFoQkqZMJQpLUyQQhSeq0bPsghr0e/qIjDt7JkUjSbLIFIUnqZIKQJHUyQUiSOi3bPgiNxjEN0tJnC0KS1GmmEkSSZyW5PsmNSc6YdjyStJzNTIJIsjvwfuBo4DDghUkOm25UkrR8zVIfxBOBG6vqJoAk5wLHAtdONaolzr4ESQuZmRYEcBBw68D+prZMkjQFs9SCSEdZ/auDklOBU9vdf0ly/ZD17Q/csaMvOmnIynbQULFNgHHtGOPacbMa28zF1X4XDRvXL/Y5aJYSxCbgEQP7Dwdu2/agqjoLOGvUypJsrKo1o/6ecZjV2IxrxxjXjpvV2JZrXLN0ielSYHWSQ5LsCbwAuHDKMUnSsjUzLYiqujvJK4AvALsDZ1fVNVMOS5KWrZlJEABVdRFw0YSqG/ky1RjNamzGtWOMa8fNamzLMq5U/at+YEmSZqoPQpI0Q5ZlgpiVKT2SPCLJl5Ncl+SaJKe35X+Q5LtJrmwfz55CbDcn+WZb/8a2bL8kX0pyQ/u874RjevTAObkyyQ+TvHpa5yvJ2Um2JLl6oKzzHKXx3vYz940kh084rrcn+VZb96eT7NOWr0ryfwbO3V9MOK4F37skb2jP1/VJnjmuuBaJ7byBuG5OcmVbPpFztsj3w+Q+Y1W1rB40HeDfBg4F9gSuAg6bUiwHAoe32w8C/pFmmpE/AF435fN0M7D/NmVvA85ot88A3jrl9/F7NPdzT+V8AU8FDgeu3t45Ap4N/C3NeJ8jgQ0Tjus/ACva7bcOxLVq8LgpnK/O9679f3AVcD/gkPb/7O6TjG2bn78DOHOS52yR74eJfcaWYwvi3ik9qupnwPyUHhNXVZur6vJ2+0fAdcz26PFjgXXt9jrguCnGchTw7ar6zrQCqKqvAP+8TfFC5+hY4MPV+BqwT5IDJxVXVX2xqu5ud79GM85oohY4Xws5Fji3qn5aVf8E3Ejzf3fisSUJcCLwsXHVv0BMC30/TOwzthwTxExO6ZFkFfB4YENb9Iq2mXj2pC/ltAr4YpLL0oxeBzigqjZD8+EFHjaFuOa9gPv+h532+Zq30Dmapc/dy2j+0px3SJIrklyS5NemEE/XezdL5+vXgNur6oaBsomes22+Hyb2GVuOCaLXlB6TlOSBwCeBV1fVD4EPAI8EHgdspmneTtpTqupwmtl1T0vy1CnE0CnNQMpjgE+0RbNwvrZnJj53Sd4I3A18tC3aDBxcVY8HXgOck+TBEwxpofduJs5X64Xc94+RiZ6zju+HBQ/tKBvpnC3HBNFrSo9JSbIHzZv/0ar6FEBV3V5VP6+qe4C/YoxN64VU1W3t8xbg020Mt883WdvnLZOOq3U0cHlV3d7GOPXzNWChczT1z12StcBzgZOqvWjdXsK5s92+jOZa/6MmFdMi793UzxdAkhXA8cB582WTPGdd3w9M8DO2HBPEzEzp0V7b/CBwXVW9c6B88Lrh84Crt33tmOPaO8mD5rdpOjivpjlPa9vD1gIXTDKuAff5i27a52sbC52jC4GXtHeaHAn8YP4ywSQkeRbweuCYqvrJQPnKNGuxkORQYDVw0wTjWui9uxB4QZL7JTmkjevrk4prwDOAb1XVpvmCSZ2zhb4fmORnbNw98bP4oOnt/0eazP/GKcbxqzRNwG8AV7aPZwMfAb7Zll8IHDjhuA6luYPkKuCa+XMEPBRYD9zQPu83hXP2AOBO4CEDZVM5XzRJajPw/2j+ejtloXNE0/x/f/uZ+yawZsJx3UhzfXr+c/YX7bH/sX2PrwIuB35zwnEt+N4Bb2zP1/XA0ZN+L9vyDwEv3+bYiZyzRb4fJvYZcyS1JKnTcrzEJEnqwQQhSepkgpAkdTJBSJI6mSAkSZ1MENKAJAckOSfJTe00I/87yfOSPC3J56YdnzRJJgip1Q5M+gzwlao6tKr+Hc1AyolPbCfNAhOEtNXTgZ9V1b3z+1fVd6rqzwcPatcweN3A/tXtZGokeUk78dxVST7Slv1ikvVt+fokB7flJ7SvvSrJV9qy3dOs3XBpe/xvj/1fLS1gptaklqbsl2hGxg4lyS/RjP59SlXdkWS/9kfvo5mGeV2SlwHvpZmi+UzgmVX13bQL+NCMLv5BVT0hyf2Aryb5YjVTXksTZQtCWkCS97d/3V/a8yVPB86vqjsAqmp+fYEnAee02x+hmUIB4KvAh5L8Fs0CSNDMe/WSNKuXbaCZVmH1aP8SaTi2IKStrqGZZweAqjotyf7Axm2Ou5v7/nG1V/sc+k2vPD+T6suTHAE8B7gyyePa3/HKqvrCcP8EaeexBSFt9b+AvZL8zkDZAzqOu5lmeUradX8PacvXAycmeWj7s/lLTP9A09kNcBLw9+3PH1lVG6rqTOAOmqmavwD8TjvNM0ke1c6oK02cLQipVVWV5DjgXUl+D5gDfkwzTfagT7L1MtClNDMDU1XXJPlj4JIkPweuAF4KvAo4O8nvtr/z5Pb3vD3JappWw3qa2UG/QbPm8eXtXVVzTHdpVy1jzuYqSerkJSZJUicThCSpkwlCktTJBCFJ6mSCkCR1MkFIkjqZICRJnUwQkqRO/x+A39KwAyOs+QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train.Glucose,kde = False)\n",
    "plt.xlabel('Glucose')\n",
    "plt.ylabel('Number of occurrences')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Outcome',y='Glucose',data = train, hue=\"Outcome\")\n",
    "plt.xlabel('Diabetes' ,fontsize = 12)\n",
    "plt.ylabel('Glucose' , fontsize = 12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BloodPressure\n",
    "舒张压"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\AI\\Environment\\Anaconda2\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'frequency')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train.BloodPressure,kde = False)\n",
    "plt.xlabel('BloodPressure')\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "查看舒张压与是否糖尿病之间的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x = 'Outcome',y = 'BloodPressure',data = train,hue = \"Outcome\")\n",
    "plt.xlabel('Diabetes', fontsize = 12)\n",
    "plt.ylabel('BloodPressure', fontsize =12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SkinThickness\n",
    "三头肌皮肤褶层厚度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Distribution of SkinThickness')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(train.shape[0]),train[\"SkinThickness\"].values,color = 'blue')\n",
    "plt.title(\"Distribution of SkinThickness\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\AI\\Environment\\Anaconda2\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'frequency')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train.SkinThickness, kde = False)\n",
    "plt.xlabel('SkinThickness')\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "查看三头肌皮肤褶层厚度与是否糖尿病之间的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x = 'Outcome',y = 'SkinThickness',data = train,hue=\"Outcome\")\n",
    "plt.xlabel('Diabetes', fontsize = 12)\n",
    "plt.ylabel('SkinThickness',fontsize = 12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Insulin\n",
    "2 小时血清胰岛素含量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\AI\\Environment\\Anaconda2\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'frequency')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train.Insulin,kde = False)\n",
    "plt.xlabel('Insulin')\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Insulin与是否得糖尿病之间的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x = 'Outcome', y = 'Insulin',data = train, hue = \"Outcome\")\n",
    "plt.xlabel('Diabetes',fontsize = 14)\n",
    "plt.ylabel('Insulin',fontsize = 14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BMI\n",
    "体重指数（体重，kg /（身高，m）^ 2）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\AI\\Environment\\Anaconda2\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'frequency')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train.BMI,kde = False)\n",
    "plt.xlabel('BMI')\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15365550>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "BMIDF = train.groupby(['BMI','Outcome'])['BMI'].count().unstack('Outcome').fillna(0)\n",
    "BMIDF[[0,1]].plot(kind = 'bar', stacked = True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  DiabetesPedigreeFunction\n",
    "糖尿病家族史"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\AI\\Environment\\Anaconda2\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'frequency')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train.DiabetesPedigreeFunction, kde = False)\n",
    "plt.xlabel('DiabetesPedigreeFunction')\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x182ed828>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DPFDF = train.groupby(['DiabetesPedigreeFunction','Outcome'])['DiabetesPedigreeFunction'].count().unstack('Outcome').fillna(0)\n",
    "DPFDF[[0,1]].plot(kind = 'bar',stacked = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\AI\\Environment\\Anaconda2\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'frequency')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train.Age,kde = False)\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20bbacf8>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "AgeDF = train.groupby(['Age','Outcome'])['Age'].count().unstack('Outcome').fillna(0)\n",
    "AgeDF[[0,1]].plot(kind = 'bar',stacked = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
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